For my own learning purpose

useful-basic-ml-links.md

• Linear Regression

  • A BEGINNERS GUIDE TO REGRESSION TECHNIQUES
  • Linear Regression Algorithm | Linear Regression in Python | Machine Learning Algorithm | Edureka - YouTube
  • In Depth: Linear Regression | Python Data Science Handbook
  • Linear Models - YouTube
  • Statistics 101: Linear Regression, The Very Basics - YouTube
  • How to Implement Linear Regression From Scratch in Python
  • Linear Regression using Python - Towards Data Science
  • Mathematical explanation for Linear Regression working - GeeksforGeeks
  • Gradient Descent in Linear Regression - GeeksforGeeks
  • ML | Normal Equation in Linear Regression - GeeksforGeeks
  • Univariate Linear Regression in Python - GeeksforGeeks
  • How to do Linear Regression and Logistic Regression in Machine Learning?
  • Linear Regression (Python Implementation) - GeeksforGeeks
  • ML | Multiple Linear Regression using Python - GeeksforGeeks
  • Python | Implementation of Polynomial Regression - GeeksforGeeks
  • Simple Linear Regression From Scratch in Numpy - Towards Data Science
  • A Complete Tutorial on Ridge and Lasso Regression in Python
  • Python/linear_regression.py at master · TheAlgorithms/Python
  • Python | Linear Regression using sklearn - GeeksforGeeks
  • ML | Locally weighted Linear Regression - GeeksforGeeks
  • Statistics PL15 - Multiple Regression - YouTube
  • Statistics PL18 - Nonlinear Regression - YouTube
  • Isotonic Regression is THE Coolest Machine-Learning Model You Might Not Have Heard Of

• Logistic Regression

  • Logistic Regression - YouTube
  • TLM | Logistic Regression
  • Logistic regression - Wikipedia
  • Maximum likelihood and gradient descent demonstration – Zlatan Kremonic
  • An Introduction to Logistic Regression - Towards Data Science
  • A Gentle Introduction to Logistic Regression With Maximum Likelihood Estimation
  • Logistic model - Maximum likelihood

• SVD

  • Gilbert strang - SVD
  • You Don’t Know SVD (Singular Value Decomposition)
  • SVD playlist
  • Gilbert strang - Computing Eigenvalues and Singular Values
  • Gilbert strang - Singular Value Decomposition
  • Computing the SVD
  • Lecture 47 — Singular Value Decomposition | Stanford University
  • How to Calculate the Singular-Value Decomposition (SVD) from Scratch with Python
  • What is an intuitive explanation of singular value decomposition (SVD)? - Quora
  • What is the meaning behind the singular value in Singular Value Decomposition? - Quora
  • What is the best way of introducing singular value decomposition (SVD) on a linear algebra course? Why is it so important? Are there any applications which have a real impact? - Quora
  • What's the difference between SVD and SVD++? - Quora
  • What is the purpose of Singular Value Decomposition? - Quora

• Covariance

  • Baffled by Covariance and Correlation??? Get the Math and the Application in Analytics for both the terms..
  • Covariance and Correlation Part 1: Covariance - YouTube
  • The Covariance Matrix : Data Science Basics
  • Statistics 101: The Covariance Matrix

• PCA

  • Implementing a Principal Component Analysis (PCA)– in Python, step by step
  • The Mathematics Behind Principal Component Analysis
  • A tutorial on Principal Components Analysis
  • Principal Component Analysis - Youtube
  • Dimensionality Reduction For Dummies — Part 1: Intuition
  • Data Analysis 6: Principal Component Analysis (PCA) - Computerphile
  • Visual Explanation of Principal Component Analysis, Covariance, SVD
  • luis serrano pca
  • StatQuest: Principal Component Analysis (PCA), Step-by-Step - YouTube
  • StatQuest: PCA in Python - YouTube
  • StatQuest: PCA - Practical Tips
  • Dimensionality reduction and PCA
  • Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)
  • Visualizing Classifier Boundaries Using Kernel PCA
  • Understanding Principal Component Analysis Once And For All
  • How to Calculate Principal Component Analysis (PCA) from Scratch in Python
  • Implementing a Principal Component Analysis (PCA)
  • What is an intuitive explanation for PCA? - Quora
  • What is an intuitive explanation of the relation between PCA and SVD? - Quora
  • Why don't people use SVD in PCA rather than eigen value decomposition? - Quora
  • (19) Statistics PL03 - Descriptive Statistics II - YouTube
  • Pca
  • Dimensionality Reduction and Principal Component Analysis (PCA) Explained
  • In Depth: Principal Component Analysis

• LDA

  • Linear Discriminant Analysis – Bit by Bit
  • Linear Discriminant Analysis
  • Linear Discriminant Analysis
  • A Geometric Intuition for Linear Discriminant Analysis
  • Machine Learning with Python: Linear Discriminant Analysis in Python
  • Using Linear Discriminant Analysis (LDA) for data Explore: Step by Step. | Blog
  • Linear Discriminant Analysis (LDA) Numerical Example
  • Classification — Linear Discriminant Analysis
  • Linear Discriminant Analysis
  • Feature Reduction using — PCA & LDA
  • Machine Learning: In-Depth LDA (Linear Discriminant Analysis) Python Example On The Iris Dataset. - YouTube
  • How to implement Linear Discriminant Analysis python | +91-7307399944 for query - YouTube
  • Linear Discriminant Analysis In Python - Towards Data Science
  • Implementing LDA in Python with Scikit-Learn
  • Machine Learning with Python: Linear Discriminant Analysis in Python
  • (Linear Discriminant Analysis) using Python - Journey 2 Artificial Intelligence - Medium

• SVM

  • Please explain Support Vector Machines (SVM) like I am a 5 year old. : MachineLearning
  • Statquest - Support Vector Machines, Clearly Explained!!!
  • Coursera Support vector machine
  • Intuition for the Support Vector Machine (primal form)
  • Chapter 2 : SVM (Support Vector Machine) Theory - Machine Learning 101 - Medium
  • Understanding Support Vector Machines algorithm (along with code)
  • Support Vector Machines in Scikit-learn (article) - DataCamp
  • Implementing SVM and Kernel SVM with Python Scikit-Learn
  • Support Vector Machines for Machine Learning
  • cs229-notes3.dvi
  • Classifying data using Support Vector Machines(SVMs) in Python - GeeksforGeeks
  • Support Vector Machine (SVM) - Fun and Easy Machine Learning
  • In-Depth: Support Vector Machines | Python Data Science Handbook
  • Sentdex - Support Vector Machine Intro and Application - YouTube
  • Support Vector Machine — Introduction to Machine Learning Algorithms
  • Classification From Scratch, Part 7 of 8: SVM - DZone Big Data
  • Chapter 3.1 : SVM from Scratch in Python. - Deep Math Machine learning.ai - Medium
  • adityajn105/SVM-From-Scratch: An Implementation of SVM - Support Vector Machines using Linear Kernel
  • SVM-From-Scratch/Support Vector Machine From Scratch.ipynb at master · adityajn105/SVM-From-Scratch

• Decision Trees

  • StatQuest: Decision Trees
  • Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
  • Classification And Regression Trees for Machine Learning
  • How To Implement The Decision Tree Algorithm From Scratch In Python
  • Clas - 5 Data Science Training | Decision Tree Classifier Explained | Edureka
  • Understanding Decision Trees for Classification in Python
  • A Simple Explanation of Information Gain and Entropy
  • A Simple Explanation of Gini Impurity
  • In-Depth: Decision Trees and Random Forests
  • The Simple Math behind 3 Decision Tree Splitting criterions

  Entropy sum

  • Decision tree Learning example | ID3 |
  • (137) Decision Tree Classification Algorithm – Solved Numerical Question 2 in Hindi - YouTube
  • ID3
  • Decision Trees for Classification: A Machine Learning Algorithm | Xoriant Blog

  GINI sum

  • (137) Decision Tree Solved Example Using CART Model in Hindi | Data mining | Machine Learning | AI - YouTube
  • Gini Index For Decision Trees

• Feature Selection

  • How do I select features for Machine Learning? - YouTube
  • Feature Selection in Machine Learning using Python - YouTube
  • Hands-on with Feature Selection Techniques: An Introduction
  • How to Choose a Feature Selection Method For Machine Learning
  • Feature Selection For Machine Learning in Python
  • FEATURE SELECTION Techniques for Classification Models
  • 1.13. Feature selection — scikit-learn 0.22.2 documentation
  • anujdutt9/Feature-Selection-for-Machine-Learning: Methods with examples for Feature Selection during Pre-processing in Machine Learning.

• Evaluation metrics

  • MLM- A Gentle Introduction to Model Selection for Machine Learning
  • Data Preprocessing : Concepts
  • About Feature Scaling and Normalization
  • Measuring Search Effectiveness
  • Classifier evaluation with imbalanced datasets
  • ROC
  • ROC curve 101
  • A Pirate's Guide to Accuracy, Precision, Recall, and Other Scores
  • Multi-Class Metrics Made Simple, Part II: the F1-score
  • Feature Scaling with scikit-learn

• Bagging and boosting

  • Ensemble methods: bagging, boosting and stacking
  • UDACITY: Bootstrap aggregating bagging
  • Bagging, boosting and stacking in machine learning
  • A Gentle Introduction to the Bootstrap Method
  • Ensemble methods: bagging, boosting and stacking
  • Basics of Ensemble Learning Explained in Simple English
  • A Comprehensive Guide to Ensemble Learning (with Python codes)
  • StatQuest: Random Forests Part 1 - Building, Using and Evaluating
  • Random Forests for Complete Beginners
  • Random Forest(Bootstrap Aggregation) Easily Explained
  • Selecting good features – Part III: random forests
  • Effect of Irrelevant Features
  • TheAlgorithms-random_forest_classification
  • UDACITY: Boosting
  • AdaBoost, Clearly Explained
  • Boosting and AdaBoost for Machine Learning
  • A Kaggle Master Explains Gradient Boosting
  • A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
  • Gradient Boost Part 1: Regression Main Ideas
  • Gradient boosting
  • Gradient Boosting for Linear Regression - why does it not work?
  • Boosting Machine Learning Tutorial | Adaptive Boosting, Gradient Boosting, XGBoost | Edureka
  • Xgboost (Boosting) Intuition Easily Explained
  • An End-to-End Guide to Understand the Math behind XGBoost
  • How to Develop Your First XGBoost Model in Python with scikit-learn
  • Why is the boosting algorithm robust to overfitting?
  • 30 Questions to test a data scientist on Tree Based Models
  • 45 questions to test Data Scientists on Tree Based Algorithms (Decision tree, Random Forests, XGBoost)
  • 40 Questions to ask a Data Scientist on Ensemble Modeling Techniques (Skilltest Solution)

• Graphical Models, Bayesian networks

  • A Sober Look at Bayesian Neural Networks
  • A Gentle Introduction to Bayesian Belief Networks
  • Graphical Models
  • Directed Graphical Models
  • Introduction to Bayesian Networks
  • Probabilistic Graphical Models
  • Introduction to Bayesian Networks | Implement Bayesian Networks In Python | Edureka
  • Bayesian Networks - YouTube
  • (24) Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning] - YouTube
  • A friendly introduction to Bayes Theorem and Hidden Markov Models - YouTube(24) belief network - YouTube
  • (24) Bayesian Belief Network Explained with Solved Example in Hindi - YouTube
  • (24) BayesianNetworks - YouTube

• Clustering

  • The Most Comprehensive Guide to K-Means Clustering You’ll Ever Need
  • Beginner’s Guide To K-Means Clustering
  • K-means Clustering Algorithm: Know How It Works
  • In Depth: k-Means Clustering
  • TheAlgorithms - k_means_clust
  • K means Clustering – Introduction
  • Image compression using K-means clustering
  • Introduction to Image Segmentation with K-Means clustering
  • ML | Mini Batch K-means clustering algorithm
  • Clustering in Machine Learning
  • Different Types of Clustering Algorithm
  • In Depth: Gaussian Mixture Models
  • In-Depth: Kernel Density Estimation
  • A Beginner’s Guide to Hierarchical Clustering and how to Perform it in Python
  • ML | Hierarchical clustering (Agglomerative and Divisive clustering)
  • Hierarchical Clustering / Dendrogram: Simple Definition, Examples
  • Hierarchical Clustering / Dendrograms
  • ML | Mean-Shift Clustering
  • ML | Spectral Clustering
  • ML | Fuzzy Clustering
  • DBSCAN Clustering in ML | Density based clustering
  • How DBSCAN works and why should we use it?
  • What is the Jaccard Index?
  • K-Means Clustering Implementation in Python | Kaggle
  • Python Programming Tutorials
  • Machine Learning Workflows in Python from Scratch Part 2: k-means Clustering
  • K-Means Clustering in Python - Blog by Mubaris NK
  • Implementing K Means Clustering from Scratch - in Python - The Nadig Blog
  • Machine Learning Workflows in Python from Scratch Part 1: Data Preparation
  • Wikipedia - Single linkage Clustering
  • Pyclustering

• Naive bayes

  • Luis Serrano - Naive Bayes classifier: A friendly approach
  • Andrew Ng Naive Bayes Generative Learning Algorithms
  • Andrew Ng Naive Bayes Text Clasification
  • 3blue1brown- Bayes theorem, and making probability intuitive
  • 3blue1brown- The quick proof of Bayes' theorem
  • Brandon Rohrer - How Bayes Theorem works
  • Naive Bayes - Georgia Tech - Machine Learning
  • The Bayesian Trap
  • In Depth: Naive Bayes Classification
  • Naive Bayes Classifier in Python | Naive Bayes Algorithm | Machine Learning Algorithm | Edureka
  • How to Develop a Naive Bayes Classifier from Scratch in Python
  • Naive Bayes Classifier From Scratch
  • kaggle - Naive Bayes Classifier¶
  • Naive Bayes Classifiers
  • Understanding Naive Bayes Classifier from scratch : Python code
  • kDnuggets - Naive Bayes from Scratch using Python only – No Fancy Frameworks
  • Naïve Bayes for Machine Learning – From Zero to Hero
  • How Bayes’ Theorem is Applied in Machine Learning

• MLE, MAP, Mixture models, expectation maximization

  • The Only Theorem Data Scientists Need To Know
  • Pieter abeel Maximum Likelihood Examples\
  • Gaussians.pdf
  • normal distribution - Maximum Likelihood Estimators - Multivariate Gaussian - Cross Validated
  • (9) Maximum Likelihood estimation - an introduction part 1 - YouTube
  • Maximum Likelihood Estimation Explained - Normal Distribution
  • Probability concepts explained: Maximum likelihood estimation
  • Maximum likelihood estimation of normal distribution - Daijiang Li
  • cs229-notes2.pdf
  • An Introductory Guide to Maximum Likelihood Estimation (with a case study in R)
  • YOUTUBE - MLE
  • Gaussian Mixture Model - GeeksforGeeks
  • 5.pdf
  • Gaussian Mixture Models Clustering Algorithm Explained
  • analytics vidya GMM
  • Hidden markov model-GMM-tf2
  • MLM-A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning
  • MLM-A Gentle Introduction to Monte Carlo Sampling for Probability
  • Gaussian Mixture Model clustering: how to select the number of components (clusters)
  • A Novel Ship Detector Based on Gaussian Mixture Model and K-Means Algorithm | SpringerLink
  • Probability concepts explained: Bayesian inference for parameter estimation.

• EXPECTATION MAXIMIZATION

  • MLM-A Gentle Introduction to Expectation-Maximization (EM Algorithm)
  • Edureka expectation maximization
  • YOUTUBE - EXPECTATION MAXIMIZATION
  • YOUTUBE-EM
  • Gaussian Mixture Model - GeeksforGeeks
  • Gaussian Mixture Models Clustering Algorithm Explained
  • What are Gaussian Mixture Models? A Powerful Clustering Algorithm
  • A Gentle Introduction to Expectation-Maximization (EM Algorithm)
  • EM Algorithm In Machine Learning | Expectation-Maximization | Machine Learning Tutorial | Edureka - YouTube
  • (ML 16.3) Expectation-Maximization (EM) algorithm - YouTube
  • Mixture Models - YouTube
  • Expectation Maximization Algorithm - YouTube
  • Clustering - YouTube
  • Clustering (4): Gaussian Mixture Models and EM - YouTube
  • Expectation Maximization with an Example – Stokastik
  • ML | Expectation-Maximization Algorithm - GeeksforGeeks
  • EM Algorithm (Expectation-maximization): Simple Definition - Statistics How To
  • A Tutorial on the Expectation Maximization (EM) Algorithm